AI-powered bidding is one of the most popular and valuable applications of artificial intelligence in advertising today, yet it’s an extremely complex technology to master, so few people truly know how it works and how it improves Adwords campaigns. It all becomes clear and easy enough to utilize, though, if you have a basic understanding of the options and how they operate. Here’s an overview of everything you need to know about AI-powered bidding algorithms to take advantage of the benefits.
What is Automated Bidding?
Automated bidding is designed to help advertisers set bids without resorting to guesswork. Google Ads offers a variety of bid strategies designed to help you meet specific business goals. Say, for example, you have a target cost-per-acquisition (CPA) goal and want to maximize conversions. You can utilize Google’s target CPA bid strategy to meet your goal. This allows Google Ads to automatically make changes to bids using machine learning insights.
Smart Bidding is a subset of Google automated bidding that offers conversion-based bid strategies: Target CPA, Target ROAS and Enhanced CPC. This allows you to make targeted bid decisions for each auction your ads enter. These automated strategies use a number of important signals to make bid decisions, including device, location, time of day, remarketing list, language, operating system, and more.
Google Automated Bid Strategies
Currently, Google offers six main automated bidding strategies advertisers can use to work towards key business goals. Here’s a brief overview of how each work for PPC bid automation:
AI-powered Bidding Algorithms – A Walkthrough
Adwords artificial intelligence is just one form of AI bidding technology. There are many other PPC bid automation tools with advanced bidding algorithms for ads. To get a fuller understanding of how AI-powered bidding algorithms work, here’s a walkthrough of the QuanticMind bidding process:
Step 1: Estimate the Value of Each Keyword
All AI bidding technology begins by determining the optimal value of each keyword you bid on in a campaign. There are different metrics the technology can use to determine this, such as impressions or conversions. QuanticMind uses a revenue-per-click (RPC) model – determining how much to value each keyword based on the potential revenue they drive. Using all relevant business data and bid landscape data, the RPC model generates machine learning algorithms to understand millions of interactions and how they might impact performance.
Step 2: Model Different Bidding Scenarios
After assigning value to each keyword, QuanticMind determines how changes in CPC bids might impact clicks and costs. Using bid landscape data, it maps out what click volume might look like for each hypothetical cost. This array of bids is fed into QuanticMind’s decision engine where it’s used to determine final bid amounts.
Step 3: Determine the Best CPC
Next, the algorithm determines the best bid for each keyword based on the performance goal you specify. This works much like the goal-oriented strategies you select with Adwords artificial intelligence. For example, if your goal is to maximize profit margin, the overall maximum margin for a group of keywords is determined by finding the CPC with a maximum margin for each individual keyword.
Step 4: Calculate Bid Modifiers
In the next step, QuanticMind looks at historical performance data for bid modifier opportunities. This includes leveraging location, device, audience, and other important dimensions to create bid adjustments and improve results. Using the same machine learning and AI bidding technology, bid modifiers for location, device and audience are automatically adjusted through the automated bidding platform. This kind of automation is extremely valuable because it ensures you only bid exactly what you need on various dimensions to meet your advertising goals. This improves campaign efficiency and efficacy, driving higher ROAS.
Step 5: Anomaly Detection
One problem that makes people nervous about using bidding algorithms for their ads is the potential for mistakes. AI bidding relies on data inputs to make informed bidding decisions. What if there’s a problem with the data? The algorithms could end up making less-than-optimum bidding decisions based on this. With manual bidding, you always have an account manager checking performance and ensuring there are no data quality issues.
QuanticMind addresses this problem with built-in anomaly detection for AI bidding. It uses several techniques to compare forecasted cost, revenue, clicks and CPC to actual results. If performance starts to deviate from expectations, bidding is automatically paused until the issue is addressed.
Step 6: Bid Push
Lastly, these final optimized bidding decisions are applied to Google Ads. These highly optimized bids are based on all relevant historical performance data, business goals, and more. If your business has additional data you can use to optimize bids throughout the day, these will also be applied retrospectively. Examples include inventory data or other limitations to costs, leads, or revenue.
All these processes take place in the background. PPC managers are free to monitor and adjust changes to bids and accounts, but it’s possible to optimize everything with high-quality business data and AI bidding technology.
Why Use Bidding Algorithms for Your Ads?
There are lots of benefits to using bidding algorithms for your PPC ads in 2019:
Advanced machine learning
Smart Bidding is able to understand how different bids might impact conversions. Machine learning algorithms are able to learn from data and a wide range of parameters to understand potential performance. This capacity goes far beyond what a regular PPC manager or even a team of data scientists could manage.
Automated bidding is capable of making optimizations based on the latest insights at the time of bidding. This includes dimensions such as location, intent, weekday and time of day, demographics, site behavior, operating system, and more. AI bidding is capable of making targeted bid adjustments at the time of auction to maximize the value of these data insights for bidding. Advertisers who don’t take advantage of bid automation are unable to effectively target valuable micro-moments when people turn to search engines because they’re looking to buy.
AI bidding allows you to select a targeted marketing goal to control how the algorithms make bidding decisions. It also allows you to customize performance control settings, including selecting the right attribution model for you.
Advanced performance reporting
AI bidding technology makes it easy to get a snapshot of your bid strategy status and performance. You can also easily create bidding experiments to see for yourself how strategy changes might impact performance. Forecasting capabilities illustrate how many conversions your ads might receive for different CPA targets.
Artificial intelligence is a valuable tool for Adwords advertisers today. AI-powered bidding algorithms make it possible to make targeted campaign decisions, improving budget spend while meeting business goals. Google Ads offers a number of internal AI bidding features that encourage businesses to try out automation to see the benefits for themselves. But third-party tools like QuanticMind actually have advanced capabilities that allow advertisers to maximize the benefits of AI bidding and machine learning. The fact that businesses need to utilize AI PPC bid automation in 2019 is a given. The next thing to figure out is which platform has the right features and the most value to help you meet your advertising goals.